In [232]:
import pandas as pd
from scipy.stats import binom_test, fisher_exact
from genepy.utils import helper as h 
from genepy.utils import plot
from genepy.imaging import fish
from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import fisher_exact, chi2_contingency, ttest_ind
from scipy.spatial import distance_matrix
import numpy as np

%load_ext autoreload
%autoreload 2
%load_ext rpy2.ipython
The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
The rpy2.ipython extension is already loaded. To reload it, use:
  %reload_ext rpy2.ipython

Processing the data

In [2]:
project='FishSuperResColoc'
version='v2'
In [3]:
rename = {
"Text Between Delimiters":"subset",
"Folder Short":"folder_short",
"Folder Path":"folder",
"Source.Name":"name",
"ImageDocumentName::Image Name":"image",
"ParentID::ID of the parent!!I":"parent_id",
"ID::ID!!I":"id",
"RegionsCount::Count!!I": "count",
"ImageSceneName::Image Scene Name ":"scene",
"Area1::Area Unscaled!!R":"area_u",
"Area::Area!!R": "area",
"CenterX1::Center X Unscaled!!R":"x",
"CenterY1::Center Y Unscaled!!R":"y",
"ImageScaleX::Image Scale X!!R":"scale_x",
"ImageScaleY::Image Scale Y!!R":"scale_y",
"ImageIndexZ::Image Index Z!!I":"z",
'Classes 5 RegionsCount ::Classes 5 Count!!I':"count_red",
'Classes 9 RegionsCount ::Classes 9 Count!!I':"count_green",
"ClassColorName::Region Class Color Name": "class",
"IntensityMaximum_TV1-T1-SR::Intensity Maximum of channel 'TV1-T1-SR'!!R":"max_red",
"IntensityMaximum_TV2-T1-SR::Intensity Maximum of channel 'TV2-T1-SR'!!R":"max_tv2_t1",
"IntensityMaximum_TV2-T2-SR::Intensity Maximum of channel 'TV2-T2-SR'!!R":"max_green",
"IntensityMaximum_TV2-T3-SR::Intensity Maximum of channel 'TV2-T3-SR'!!R":"max_dapi",
"IntensityMean_TV1-T1-SR::Intensity Mean Value of channel 'TV1-T1-SR'!!R":"mean_red",
"IntensityMean_TV2-T1-SR::Intensity Mean Value of channel 'TV2-T1-SR'!!R":"mean_tv2_t1",
"IntensityMean_TV2-T2-SR::Intensity Mean Value of channel 'TV2-T2-SR'!!R":"mean_green",
"IntensityMean_TV2-T3-SR::Intensity Mean Value of channel 'TV2-T3-SR'!!R":"mean_dapi",
"IntensityMinimum_TV1-T1-SR::Intensity Minimum of channel 'TV1-T1-SR'!!R":"min_red",
"IntensityMinimum_TV2-T1-SR::Intensity Minimum of channel 'TV2-T1-SR'!!R":"min_tv2_t1",
"IntensityMinimum_TV2-T2-SR::Intensity Minimum of channel 'TV2-T2-SR'!!R":"min_green",
"IntensityMinimum_TV2-T3-SR::Intensity Minimum of channel 'TV2-T3-SR'!!R":"min_dapi",
"IntensitySum0_TV1-T1-SR::Intensity Pixel Count of channel 'TV1-T1-SR'!!R":"pixsum_red",
"IntensitySum0_TV2-T1-SR::Intensity Pixel Count of channel 'TV2-T1-SR'!!R":"pixsum_tv2_t1",
"IntensitySum0_TV2-T2-SR::Intensity Pixel Count of channel 'TV2-T2-SR'!!R":"pixsum_green",
"IntensitySum0_TV2-T3-SR::Intensity Pixel Count of channel 'TV2-T3-SR'!!R":"pixsum_dapi",
"IntensityRange_TV1-T1-SR::Intensity Range of channel 'TV1-T1-SR'!!R":"range_red",
"IntensityRange_TV2-T1-SR::Intensity Range of channel 'TV2-T1-SR'!!R":"range_tv2_t1",
"IntensityRange_TV2-T2-SR::Intensity Range of channel 'TV2-T2-SR'!!R":"range_green",
"IntensityRange_TV2-T3-SR::Intensity Range of channel 'TV2-T3-SR'!!R":"range_dapi",
"IntensityStd_TV1-T1-SR::Intensity Standard Deviation of channel 'TV1-T1-SR'!!R":"std_red",
"IntensityStd_TV2-T1-SR::Intensity Standard Deviation of channel 'TV2-T1-SR'!!R":"std_tv2_t1",
"IntensityStd_TV2-T2-SR::Intensity Standard Deviation of channel 'TV2-T2-SR'!!R":"std_green",
"IntensityStd_TV2-T3-SR::Intensity Standard Deviation of channel 'TV2-T3-SR'!!R":"std_dapi",
"IntensitySum1_TV1-T1-SR::Intensity Sum of channel 'TV1-T1-SR'!!R":"sum_red",
"IntensitySum1_TV2-T1-SR::Intensity Sum of channel 'TV2-T1-SR'!!R":"sum_tv2_t1",
"IntensitySum1_TV2-T2-SR::Intensity Sum of channel 'TV2-T2-SR'!!R":"sum_green",
"IntensitySum1_TV2-T3-SR::Intensity Sum of channel 'TV2-T3-SR'!!R":"sum_dapi",
"IntensitySum2_TV1-T1-SR::Intensity Sum Squares of channel 'TV1-T1-SR'!!R":"sum2_red",
"IntensitySum2_TV2-T1-SR::Intensity Sum Squares of channel 'TV2-T1-SR'!!R":"sum2_tv2_t1",
"IntensitySum2_TV2-T2-SR::Intensity Sum Squares of channel 'TV2-T2-SR'!!R":"sum2_green",
"IntensitySum2_TV2-T3-SR::Intensity Sum Squares of channel 'TV2-T3-SR'!!R":"sum2_dapi",
"Unnamed: 27":"unknown"
}
In [80]:
data = pd.read_csv('../data/'+project+'/data_query_files/querry.csv').rename(columns=rename)
data
Out[80]:
subset folder_short folder name image parent_id id scene area x ... sum_1 sum_2 range_1 range_2 std_1 std_2 sum1_1 sum1_2 sum2_1 sum2_2
0 Subset10 AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Image 7_Subset10_Out_Maximum intensity project... NaN NaN NaN NaN pixel² pixel ... pixel² pixel² Unknown Unknown Unknown Unknown Unknown Unknown Unknown² Unknown²
1 Subset10 AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Image 7_Subset10_Out_Maximum intensity project... Image 7_Subset10_Out_Maximum intensity project... 14.0 15.0 NaN 7 368.64285714286 ... 7 7 1661 4080 547.72403548468 1510.241245314 49094 164490 346117272 3878964986
2 Subset10 AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Image 7_Subset10_Out_Maximum intensity project... Image 7_Subset10_Out_Maximum intensity project... 14.0 16.0 NaN 28 396.21428571429 ... 28 28 8089 13818 2760.2006509669 3701.1435641623 123989 523204 754750539 10146373720
3 Subset10 AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Image 7_Subset10_Out_Maximum intensity project... Image 7_Subset10_Out_Maximum intensity project... 14.0 17.0 NaN 13 426.88461538462 ... 13 13 4583 6576 1313.3590522016 2101.7813019022 108303 285907 922971237 6340918481
4 Subset10 AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Image 7_Subset10_Out_Maximum intensity project... Image 7_Subset10_Out_Maximum intensity project... 14.0 18.0 NaN 29 340.74137931035 ... 29 29 3515 12083 1077.0374837392 3107.8328355138 196046 460225 1357791794 7574132899
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
117757 Subset9 IF FISH final\Runx2_excel\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... 1.0 23.0 NaN 55230 1146.4196089082 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
117758 Subset9 IF FISH final\Runx2_excel\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... 1.0 24.0 NaN 60384 1546.5754835718 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
117759 Subset9 IF FISH final\Runx2_excel\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... 1.0 25.0 NaN 48042 2480.9880521211 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
117760 Subset9 IF FISH final\Runx2_excel\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... 1.0 26.0 NaN 70306 1932.6927004808 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
117761 Subset9 IF FISH final\Runx2_excel\ C:\Users\M232498\Desktop\Ju\GDrive\IF FISH fin... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... Runx2_488_MYC_FISH_SIM-Orthogonal Projection-0... 1.0 27.0 NaN 68115 129.92614695735 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

117762 rows × 27 columns

In [87]:
data.columns
Out[87]:
Index(['subset', 'folder_short', 'folder', 'name', 'image', 'parent_id', 'id',
       'scene', 'area', 'x', 'y', 'max_1', 'max_2', 'mean_1', 'mean_2',
       'min_1', 'min_2', 'sum_1', 'sum_2', 'range_1', 'range_2', 'std_1',
       'std_2', 'sum1_1', 'sum1_2', 'sum2_1', 'sum2_2'],
      dtype='object')
In [5]:
cat ../data/$project/data_query_files/querry.csv | less
<U+FEFF>Text Between Delimiters,Folder Short,Folder Path,Source.Name,ImageDocumentName::Image Name,ParentID::ID of the parent!!I,ID::ID!!I,ImageSceneName::Image Scene Name ,Area1::Area Unscaled!!R,CenterX1::Center X Unscaled!!R,CenterY1::Center Y Unscaled!!R,IntensityMaximum_TV1-T1-SR::Intensity Maximum of channel 'TV1-T1-SR'!!R,IntensityMaximum_TV2-T1-SR::Intensity Maximum of channel 'TV2-T1-SR'!!R,IntensityMean_TV1-T1-SR::Intensity Mean Value of channel 'TV1-T1-SR'!!R,IntensityMean_TV2-T1-SR::Intensity Mean Value of channel 'TV2-T1-SR'!!R,IntensityMinimum_TV1-T1-SR::Intensity Minimum of channel 'TV1-T1-SR'!!R,IntensityMinimum_TV2-T1-SR::Intensity Minimum of channel 'TV2-T1-SR'!!R,IntensitySum0_TV1-T1-SR::Intensity Pixel Count of channel 'TV1-T1-SR'!!R,IntensitySum0_TV2-T1-SR::Intensity Pixel Count of channel 'TV2-T1-SR'!!R,IntensityRange_TV1-T1-SR::Intensity Range of channel 'TV1-T1-SR'!!R,IntensityRange_TV2-T1-SR::Intensity Range of channel 'TV2-T1-SR'!!R,IntensityStd_TV1-T1-SR::Intensity Standard Deviation of channel 'TV1-T1-SR'!!R,IntensityStd_TV2-T1-SR::Intensity Standard Deviation of channel 'TV2-T1-SR'!!R,IntensitySum1_TV1-T1-SR::Intensity Sum of channel 'TV1-T1-SR'!!R,IntensitySum1_TV2-T1-SR::Intensity Sum of channel 'TV2-T1-SR'!!R,IntensitySum2_TV1-T1-SR::Intensity Sum Squares of channel 'TV1-T1-SR'!!R,IntensitySum2_TV2-T1-SR::Intensity Sum Squares of channel 'TV2-T1-SR'!!R
Subset10,AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\,C:\Users\M232498\Desktop\Ju\GDrive\IF FISH final\AAVS1_CRISPR_MED1\AAVS1_CRISPR_MED1 Image7\,Image 7_Subset10_Out_Maximum intensity projection__ Green Dots.csv,,,,,pixel²,pixel,pixel,Unknown,Unknown,Unknown,Unknown,Unknown,Unknown,pixel²,pixel²,Unknown,Unknown,Unknown,Unknown,Unknown,Unknown,Unknown²,Unknown²
:
In [ ]:
data.iloc[0].tolist()
In [ ]:
set(data.folder_short)
In [ ]:
cond = {'AAVS1_CRISPR_MED1\\AAVS1_CRISPR_MED1 Image7\\':'AAVS1',
 'IF FISH final\\AAVS1_CRISPR_MEF2D\\':'AAVS1',
 'IF FISH final\\IRF2BP2_excel\\':'',
 'IF FISH final\\MEF2C_excel\\':'',
 'IF FISH final\\MEF2D_CRISPR_MED1\\':'MEF2D',
 'IF FISH final\\MEF2D_CRISPR_MEF2D\\':'MEF2D',
 'IF FISH final\\Runx2_excel\\':''}
In [ ]:
prot = {'AAVS1_CRISPR_MED1\\AAVS1_CRISPR_MED1 Image7\\':'MED1',
 'IF FISH final\\AAVS1_CRISPR_MEF2D\\':'MEF2D',
 'IF FISH final\\IRF2BP2_excel\\':'IRF2BP2',
 'IF FISH final\\MEF2C_excel\\':'MEF2C',
 'IF FISH final\\MEF2D_CRISPR_MED1\\':'MED1',
 'IF FISH final\\MEF2D_CRISPR_MEF2D\\':'MEF2D',
 'IF FISH final\\Runx2_excel\\':'RUNX2'}
In [ ]:
data = data[~data.name.str.contains('Classes')]
In [ ]:
data['condition'] = [cond[i] for i in data.folder_short]
In [ ]:
data['protein'] = [prot[i] for i in data.folder_short]
In [ ]:
data['type'] = [i.split('.')[0].split('_')[-1] for i in data.name]
In [ ]:
data['name'] = [cond[i]+'_'+prot[i] for i in data.folder_short]
In [ ]:
set(data.type)
In [ ]:
data = data[~data.id.isna()]
In [ ]:
set(data.subset)
In [ ]:
data = data.drop(columns=['scene','image','folder','folder_short'])
In [ ]:
data['subset'] = [i.split('Subset')[-1] for i in data.subset]
In [ ]:
for val in ['area','x','y','max_1','max_2','mean_1','mean_2','min_1','min_2','range_1','range_2','std_1','std_2','sum_1','sum_2','sum1_1','sum1_2','sum2_1','sum2_2']:
    data[val]= data[val].astype(float)
In [ ]:
for val in ['subset','parent_id','id']:
    data[val] = data[val].astype('int')
In [ ]:
len(data.id)

results

why we are not doing a monte carlo but just a hypthesis testing on a binomial

monte carlo would be useful in our case if we did not know the expected distribution. given a way to generate our distribution (which specific parameters can be learnt from the data) we might be able to express what would be the null hypothesis by estimating this unknown distribution. it is useful for when we don't know the exact distribution or when it is too complex to analytically solve.

However in our case we can approximate the distribution well enough by a binomial. It is true that given that the dna will not have the same density everywhere on the nucleus, that the dots will have themselves variable intensity and size, which might express or not a multiplicity of points, the distribution is not a binomial.

But you said yourself that for all purposes we could simplify these hypothesis.

I also don't think that we have enough data points to correctly estimate the monte carlo parameters.

A binomial is thus enough for this analysis

having specificities in the data needing to be modelled:

I found that the data has specific bias that still need to be taken in account while not needing to change the statistics:

the avg size of a MYC dot is 350 whereas the one of a MYC green is 45

In [ ]:
def desc(data):
    return data.sum()/data.mean(),len(data)
# for each group
res = {}
for val in set(data.name):
    print('\n',val)
    group = data[data.name==val]
    space = group[group.type=='Nucleus'].area.sum()
    totsize = group[group.type==' Green Dots'].area.sum()
                 
    coloc,s_coloc = desc(group[group.type=='MYC  Green'].area)
    outside,s_outside = desc(group[group.type=='MYC'].area)
    print('data: \n  - total nucleuses size: '+str(int(space))+'\n  - total Green dot size: '+str(int(totsize))+'\n  - counts for coloc: '+str(int(s_coloc))+'\n  - counts for not coloc: '+str(int(s_outside))) 
    p_in = totsize/space
    isin = coloc/(outside+coloc)
    res[val] = [s_coloc,s_outside]
    print('proba: (null, obs)',p_in,isin)
    print('nb of datapoints: ',s_coloc+s_outside)
    print('p_value: ',binom_test([s_coloc,s_outside],p=p_in))

from this analysis it seems clear that the colocalization happens less than 50% of the time in any condition but that we have colocalization with MYC for all analyzed proteins compared to random chance, given the data extracted by the algorithm and the assumption we made.

In [ ]:
fisher_exact([res['AAVS1_MED1'], res['MEF2D_MED1']])
In [ ]:
fisher_exact([res['AAVS1_MEF2D'], res['MEF2D_MEF2D']])

we thus have much 1.7 times more colocalization of MEF2D when MEF2D is degraded vs when it is not and 2 times less colocalization of MED1 but p_val of .1

running new version

Loading

In [676]:
project='FishSuperResColoc'
version='IRF2BP2_v2'
<Figure size 432x288 with 0 Axes>
In [694]:
csvs = ! ls ../data/micro_IRF2BP2/*/*.csv #micro_Yaser3, #micro_MEF2D_MEF2C
res = pd.DataFrame()
for val in csvs:
    v = pd.read_csv(val,).drop(index=0)
    v['filename'] = '-'.join(val.split('/')[-2].split('-')[2:])
    res = res.append(v)
res = res.rename(columns=rename)
res.parent_id = res.parent_id.astype(int)
res.id = res.id.astype(int)
res.area = res.area.astype(float)
res = res.reset_index(drop=True)

labelling

In [695]:
unstring =  ['area', 'x', 'y', "z", "scale_x", "scale_y", "count_red", "count_green", "max_red", "max_green", "max_dapi", "mean_red", "mean_green", "mean_dapi", "min_red", "min_green", "min_dapi", "pixsum_red", "pixsum_green", "pixsum_dapi", "range_red", "range_green", "range_dapi", "std_red", "std_green", "std_dapi", "sum_red", "sum_green", "sum_dapi", "sum2_red", "sum2_green", "sum2_dapi"]
zsize = 85 
toint=['parent_id',"id","area","x",'y',"z","count_red","count_green"]
torn = {'Orange':"dapi", 'Fuchsia': "green", "Yellow": 'red'}
In [696]:
res[unstring] = pd.concat([res[i].astype(str).str.replace('  ','0').replace(' ','0').replace('', '0').replace('None',"0").astype(float) for i in unstring], axis=1)

res['x']= res['x']*res["scale_x"]
res['y']= res['y']*res["scale_y"]
res["z"]= res['z']*zsize
res = res.drop(columns=['area_u',"scale_x",'scale_y'])
print(set(res['filename']))

imageinfo = res['image'].str.replace('_Subset.czi', '').str.replace('Subset.czi', '').str.replace('.czi', '').str.replace('Image ', '').str.replace('_SIM_Channel Alignment_P', "--").str.replace('_Out_Channel Alignment_P',"--").str.replace('_P', '--')
res['group'] = res['filename'] + "--" + imageinfo
res['exp'] = ['-'.join(i.split('-')[:2]+i.split('-')[3:]) for i in res.filename]
res['treat'] = [i.split('-')[-2] for i in res.filename]
res['image'] = [i.split('--')[0] for i in imageinfo]
res['part'] = [i.split('--')[-1] for i in imageinfo]
print('number of exp')
print(set(res.exp))

res['class'] = res['class'].replace(torn)
res = res.drop(index=res[res['class'].isna()].index)
res[toint] = res[toint].astype(int)
{'IRF2BP2-MYC-VHL-G1', 'IRF2BP2-MYC-DMSO-G1'}
<ipython-input-696-ec464c123a7d>:9: FutureWarning: The default value of regex will change from True to False in a future version.
  imageinfo = res['image'].str.replace('_Subset.czi', '').str.replace('Subset.czi', '').str.replace('.czi', '').str.replace('Image ', '').str.replace('_SIM_Channel Alignment_P', "--").str.replace('_Out_Channel Alignment_P',"--").str.replace('_P', '--')
number of exp
{'IRF2BP2-MYC-G1'}
In [700]:
res.to_csv('../results/'+project+"/"+version+"_all.csv")
In [356]:
res = pd.read_csv('../results/'+project+"/"+version+"_all.csv", index_col=0)

colocallizing

In [13]:
#mkdir ../results/FishSuperResColoc
In [250]:
workon = ['MEF2C-MYC_MEF2D-G2', 'MEF2D-MEF2C-G2']
res = res[res.exp.isin(workon)]
In [701]:
call_scale = 1.2

mincellzstack = 20
minredzstack = 3
mingreenzstack = 2

minsumred = 10**6
In [702]:
cells = res[res["parent_id"]==1].copy()
dots = res[res["parent_id"]!=1].copy()
cells[['count_red','count_green']].mean()

todrop=["parent_id", "id", 'count_red', 'count_green', "filename"]
groupdefault={
    "image": "first",
    "z": ["mean", "min", "max"],
    'area': ["sum", "min", "max"],
    "class": "unique",
    "group" : "first",
    "exp" : "first",
    "treat" : "first",
    "part" : "first",
    "mean_red" : ["mean", "var"],
    "mean_green" : ["mean", "var"],
    "mean_dapi" :["mean", "var"],
    "pixsum_red" : "sum",
    "pixsum_green" : "sum",
    "pixsum_dapi" : "sum",
    "sum_red" : "sum",
    "sum_green" : "sum",
    "sum_dapi" : "sum",
}

nofilter = ['MEF2D-MEF2C-G1','MEF2D-MEF2C-G2']

coloc red

In [705]:
ared = fish.colocalize(dots[dots['class']=='red'], distance_scale=call_scale)
mred = fish.mergeAnnotated(ared, groupdefault=groupdefault, todrop=todrop)

#filtering 
torm = []
for val in set(mred[~mred.exp.isin(nofilter)].group):
    torm.extend(mred[mred.group==val].sort_values(by='sum_red_sum').index.tolist()[3:])
    torm.extend(mred[(mred.group==val) & ((mred['counts_']<minredzstack)|(mred.sum_red_sum<minsumred))])
mred = mred[~mred.index.isin(torm)]

# applying filtered red to dots:
dots = dots[~dots.index.isin(ared[~ared.m_id.isin(mred.index.tolist())].index.tolist())]
ared = ared[ared.m_id.isin(mred.index.tolist())]
IRF2BP2-MYC-DMSO-G1--6--3-1
IRF2BP2-MYC-DMSO-G1--6--4-2
IRF2BP2-MYC-DMSO-G1--6--1-2
IRF2BP2-MYC-VHL-G1--25--3-10
IRF2BP2-MYC-DMSO-G1--6--3-2
IRF2BP2-MYC-VHL-G1--25--3-13
/home/jeremie/genepy/genepy/imaging/fish.py:108: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  for val in np.tril(dist < maxdist):
/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(loc, value, pi)
IRF2BP2-MYC-DMSO-G1--6--6-2
IRF2BP2-MYC-DMSO-G1--6--3-5
IRF2BP2-MYC-DMSO-G1--6--9-5
IRF2BP2-MYC-VHL-G1--25--1-6
IRF2BP2-MYC-DMSO-G1--6--8-3
IRF2BP2-MYC-DMSO-G1--6--7-1
IRF2BP2-MYC-DMSO-G1--6--9-1
IRF2BP2-MYC-VHL-G1--25--1-3
IRF2BP2-MYC-VHL-G1--25--3-4
IRF2BP2-MYC-VHL-G1--25--1-1
IRF2BP2-MYC-DMSO-G1--6--3-7
IRF2BP2-MYC-VHL-G1--25--3-7
IRF2BP2-MYC-VHL-G1--25--3-5
IRF2BP2-MYC-DMSO-G1--6--9-2
IRF2BP2-MYC-VHL-G1--25--4-2
IRF2BP2-MYC-DMSO-G1--6--9-4
IRF2BP2-MYC-DMSO-G1--6--3-6
IRF2BP2-MYC-VHL-G1--25--2-2
IRF2BP2-MYC-VHL-G1--25--3-3
IRF2BP2-MYC-VHL-G1--25--3-9
IRF2BP2-MYC-VHL-G1--25--3-12
IRF2BP2-MYC-DMSO-G1--6--6-1
IRF2BP2-MYC-DMSO-G1--6--2-1
IRF2BP2-MYC-VHL-G1--25--2-3
IRF2BP2-MYC-DMSO-G1--6--2-4
IRF2BP2-MYC-DMSO-G1--6--3-3
IRF2BP2-MYC-DMSO-G1--6--9-6
IRF2BP2-MYC-DMSO-G1--6--7-2
IRF2BP2-MYC-VHL-G1--25--3-11
IRF2BP2-MYC-DMSO-G1--6--5-2
IRF2BP2-MYC-DMSO-G1--6--6-4
IRF2BP2-MYC-VHL-G1--25--3-6
IRF2BP2-MYC-VHL-G1--25--3-1
IRF2BP2-MYC-VHL-G1--25--1-2
IRF2BP2-MYC-DMSO-G1--6--2-3
IRF2BP2-MYC-DMSO-G1--6--4-1
IRF2BP2-MYC-VHL-G1--25--4-1
IRF2BP2-MYC-VHL-G1--25--3-2
IRF2BP2-MYC-DMSO-G1--6--5-1
IRF2BP2-MYC-VHL-G1--25--1-4
IRF2BP2-MYC-VHL-G1--25--2-4
IRF2BP2-MYC-DMSO-G1--6--1-3
IRF2BP2-MYC-VHL-G1--25--2-1
IRF2BP2-MYC-VHL-G1--25--4-4
IRF2BP2-MYC-DMSO-G1--6--2-2
IRF2BP2-MYC-DMSO-G1--6--9-3
IRF2BP2-MYC-DMSO-G1--6--8-2
IRF2BP2-MYC-VHL-G1--25--3-8

coloc green

In [ ]:
agreen = fish.colocalize(dots[dots['class']=='green'], distance_scale=call_scale, )
mgreen = fish.mergeAnnotated(agreen, groupdefault=groupdefault, todrop=todrop)

# applying filtered green to dots:
dots = dots[~dots.index.isin(agreen[~agreen.m_id.isin(mgreen.index.tolist())].index.tolist())]
agreen = agreen[agreen.m_id.isin(mgreen.index.tolist())]
IRF2BP2-MYC-DMSO-G1--6--3-1
/home/jeremie/genepy/genepy/imaging/fish.py:108: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  for val in np.tril(dist < maxdist):
/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(loc, value, pi)
IRF2BP2-MYC-DMSO-G1--6--4-2
IRF2BP2-MYC-DMSO-G1--6--1-2
IRF2BP2-MYC-DMSO-G1--6--6-3
IRF2BP2-MYC-DMSO-G1--6--3-2
IRF2BP2-MYC-DMSO-G1--6--6-2
IRF2BP2-MYC-DMSO-G1--6--3-5
IRF2BP2-MYC-DMSO-G1--6--9-5
IRF2BP2-MYC-DMSO-G1--6--8-3
IRF2BP2-MYC-DMSO-G1--6--7-1
IRF2BP2-MYC-DMSO-G1--6--9-1
IRF2BP2-MYC-DMSO-G1--6--3-7
IRF2BP2-MYC-DMSO-G1--6--9-2
IRF2BP2-MYC-DMSO-G1--6--9-4
IRF2BP2-MYC-DMSO-G1--6--3-6
IRF2BP2-MYC-DMSO-G1--6--6-1
IRF2BP2-MYC-DMSO-G1--6--2-1
IRF2BP2-MYC-DMSO-G1--6--8-1
IRF2BP2-MYC-DMSO-G1--6--2-4
IRF2BP2-MYC-DMSO-G1--6--3-3
IRF2BP2-MYC-DMSO-G1--6--9-6
IRF2BP2-MYC-DMSO-G1--6--1-1
IRF2BP2-MYC-DMSO-G1--6--7-2
IRF2BP2-MYC-DMSO-G1--6--5-2
IRF2BP2-MYC-DMSO-G1--6--6-4
IRF2BP2-MYC-DMSO-G1--6--2-3
IRF2BP2-MYC-DMSO-G1--6--4-1
IRF2BP2-MYC-DMSO-G1--6--5-1
IRF2BP2-MYC-DMSO-G1--6--3-4
IRF2BP2-MYC-DMSO-G1--6--1-3
IRF2BP2-MYC-DMSO-G1--6--2-2
IRF2BP2-MYC-DMSO-G1--6--9-3
IRF2BP2-MYC-DMSO-G1--6--8-2

coloc all

In [ ]:
adots = fish.colocalize(dots, distance_scale=call_scale, )
mdots = fish.mergeAnnotated(adots, groupdefault=groupdefault, todrop=todrop)

compute coloc ID on solo green/red dots:

In [ ]:
ared['coloc_id'] = None
agreen['coloc_id'] = None
for k, val in mdots[mdots["class"]=="cobinding"].iterrows():
    a = adots[adots.m_id==k].index
    ared.loc[set(a)&set(ared.index),"coloc_id"] = k
    mred.loc[set(mred.index) & set(ared.loc[set(a)&set(ared.index)].m_id), "coloc_id"]= k
    agreen.loc[set(a)&set(agreen.index),"coloc_id"] = k
    mgreen.loc[set(mgreen.index) & set(agreen.loc[set(a)&set(agreen.index)].m_id), "coloc_id"]= k
In [ ]:
sns.scatterplot(data=adots[(adots.group=="MED1-MYC_MEF2D-VHL-G1--11--1-2_2") & (adots.x> 6000) & abs(adots.x<6800) & (adots.y>6000) & (adots.y<6900)], x="x", y="y", hue="m_id")
In [ ]:
adots.to_csv('../results/'+project+'/'+version+'_annotated_all.csv.gz')
ared.to_csv('../results/'+project+'/'+version+'_annotated_red.csv.gz')
agreen.to_csv('../results/'+project+'/'+version+'_annotated_green.csv.gz')
mdots.to_csv('../results/'+project+'/'+version+'_aggrated_all.csv.gz')
mred.to_csv('../results/'+project+'/'+version+'_aggregated_red.csv.gz')
mgreen.to_csv('../results/'+project+'/'+version+'_aggrated_green.csv.gz')
In [7]:
adots = pd.read_csv('../results/'+project+'/'+version+'_annotated_all.csv.gz')
ared = pd.read_csv('../results/'+project+'/'+version+'_annotated_red.csv.gz')
agreen = pd.read_csv('../results/'+project+'/'+version+'_annotated_green.csv.gz')
mdots = pd.read_csv('../results/'+project+'/'+version+'_aggrated_all.csv.gz')
mred = pd.read_csv('../results/'+project+'/'+version+'_aggregated_red.csv.gz')
mgreen = pd.read_csv('../results/'+project+'/'+version+'_aggregated_green.csv.gz')

coloc cells

In [ ]:
cells = cells.drop(columns=["max_red", "max_green", "max_dapi", "mean_red", "mean_green", "mean_dapi", "min_red", "min_green", "min_dapi", "pixsum_red", "pixsum_green", "pixsum_dapi", "range_red", "range_green", "range_dapi", "std_red", "std_green", "std_dapi", "sum_red", "sum_green", "sum_dapi", "sum2_red", "sum2_green", "sum2_dapi", "parent_id", "id"])

grouping = {i: "first" for i in cells.columns}
grouping.update({
    "area": ["sum","min","max"],
    "x": "mean",
    "y": "mean",
    "z": ["max", "min"],
    "count_red": "sum",
    "count_green": "sum",
})
groups = cells.groupby('group')
counts = groups['image'].count()
mcells = groups.agg(grouping)
mcells['counts'] = counts
mcells = mcells[mcells['counts']>mincellzstack]
mcells.columns = [i[0] if "first" in i[1] else '_'.join(i) for i in mcells.columns]

compute differences (number/surface/max/min/mean) across cells

In [ ]:
folder = '../results/'+project+'/plots_'+version+'/'
! mkdir $folder
In [394]:
# compute difference in signal strength between cells &  between conditions across cells
strength = {}
for k in set(mdots.exp):
    print('\n______________________________')
    print(k)
    for val in ['mean_red_mean', 'mean_green_mean','area_sum',  'sum_green_sum',  'sum_red_sum']:
        print(val)
        a = []
        for e in ['DMSO', 'VHL']:
            print('\n')
            print(e)
            d = mdots[(mdots.exp==k)&(mdots.treat==e)]
            e = pd.DataFrame([
                [d[d['class']=="cobinding"][val].mean(), d[d['class']=="cobinding"][val].var()**(1/2)],
                [d[d['class']=="green"][val].mean(), d[d['class']=="green"][val].var()**(1/2)],
                [d[d['class']=="red"][val].mean(), d[d['class']=="red"][val].var()**(1/2)]
            ], columns=['mean','var'], index=["obs_cob", "obs_green", "obs_red"])
            print(e)
            a.append(e)
        strength[k] = e
        print("\nchange (VHL/DMSO)\n"+str(a[1]['mean']/a[0]['mean']))
______________________________
MEF2D-MYC_MEF2D-G1
mean_red_mean


DMSO
                  mean          var
obs_cob    6545.970989  1767.044356
obs_green   779.517517   627.713683
obs_red    4228.277974  1330.007729


VHL
                  mean          var
obs_cob            NaN          NaN
obs_green   208.253002   156.124475
obs_red    2756.071918  1318.437853

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.267156
obs_red      0.651819
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean         var
obs_cob    1009.405392  117.103324
obs_green  2426.165121  868.747446
obs_red     250.031436  233.218358


VHL
                  mean         var
obs_cob            NaN         NaN
obs_green  1818.794640  707.195829
obs_red      48.524732   84.508083

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.749658
obs_red      0.194075
Name: mean, dtype: float64
area_sum


DMSO
                   mean            var
obs_cob    2.634871e+06  576470.217576
obs_green  2.289120e+05  246822.865403
obs_red    1.117671e+06  968279.330045


VHL
                   mean            var
obs_cob             NaN            NaN
obs_green  1.989510e+05  251130.375544
obs_red    1.166582e+06  948177.024552

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.869115
obs_red      1.043761
Name: mean, dtype: float64
sum_green_sum


DMSO
                   mean           var
obs_cob    2.021220e+06  7.431685e+05
obs_green  7.000576e+05  1.121874e+06
obs_red    2.602758e+05  3.350646e+05


VHL
                    mean           var
obs_cob              NaN           NaN
obs_green  530527.488189  1.227291e+06
obs_red     51540.566038  7.377652e+04

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.757834
obs_red      0.198023
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob    1.692044e+07  7.165609e+06
obs_green  1.593499e+05  1.892868e+05
obs_red    5.818733e+06  6.594719e+06


VHL
                   mean           var
obs_cob             NaN           NaN
obs_green  4.249523e+04  6.875341e+04
obs_red    4.430263e+06  6.050242e+06

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.266679
obs_red      0.761379
Name: mean, dtype: float64

______________________________
MEF2D-MEF2C-G2
mean_red_mean


DMSO
                   mean          var
obs_cob    10135.384977  3218.622107
obs_green   3731.302142  2414.988979
obs_red     9599.184440  2838.665801


VHL
                  mean          var
obs_cob    8855.578485  2275.672602
obs_green  2683.933370  1855.792461
obs_red    9392.507190  2682.953757

change (VHL/DMSO)
obs_cob      0.873729
obs_green    0.719302
obs_red      0.978469
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean          var
obs_cob    2197.189243  1193.099186
obs_green  2895.283020  1173.246505
obs_red     432.187380   494.172487


VHL
                  mean          var
obs_cob    3852.919767  2407.086353
obs_green  4603.154942  2913.724670
obs_red    1857.811556  1429.493573

change (VHL/DMSO)
obs_cob      1.753568
obs_green    1.589881
obs_red      4.298625
Name: mean, dtype: float64
area_sum


DMSO
                    mean            var
obs_cob    921057.665689  752537.862872
obs_green  195295.189092  229690.635148
obs_red    331091.577325  381035.383048


VHL
                    mean            var
obs_cob    615574.745283  419984.736876
obs_green  107253.675732  118405.293540
obs_red    295266.705054  287784.840837

change (VHL/DMSO)
obs_cob      0.668335
obs_green    0.549187
obs_red      0.891798
Name: mean, dtype: float64
sum_green_sum


DMSO
                   mean           var
obs_cob    1.891263e+06  2.023966e+06
obs_green  6.116367e+05  8.603596e+05
obs_red    1.470970e+05  3.080618e+05


VHL
                   mean           var
obs_cob    2.631495e+06  4.023071e+06
obs_green  6.126602e+05  1.246082e+06
obs_red    5.587555e+05  7.956930e+05

change (VHL/DMSO)
obs_cob      1.391396
obs_green    1.001673
obs_red      3.798552
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob    1.067161e+07  1.204495e+07
obs_green  7.435049e+05  1.102672e+06
obs_red    3.549733e+06  5.341763e+06


VHL
                   mean           var
obs_cob    5.887194e+06  5.021572e+06
obs_green  3.132690e+05  5.801260e+05
obs_red    3.039867e+06  3.556385e+06

change (VHL/DMSO)
obs_cob      0.551669
obs_green    0.421341
obs_red      0.856365
Name: mean, dtype: float64

______________________________
MEF2D-MEF2C-G1
mean_red_mean


DMSO
                  mean          var
obs_cob    6430.033733  1844.648361
obs_green  1726.344314  1243.473712
obs_red    7564.666383  1460.204756


VHL
                  mean          var
obs_cob    9092.451256  2944.659316
obs_green  2884.582995  2151.117859
obs_red    9605.706021  2479.893750

change (VHL/DMSO)
obs_cob      1.414060
obs_green    1.670920
obs_red      1.269812
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean         var
obs_cob    2225.774280  866.290442
obs_green  2621.144502  778.209850
obs_red     468.088910  437.801804


VHL
                  mean          var
obs_cob    2708.350562  1882.194190
obs_green  3173.349357  1753.176895
obs_red     283.993603   478.773058

change (VHL/DMSO)
obs_cob      1.216813
obs_green    1.210673
obs_red      0.606709
Name: mean, dtype: float64
area_sum


DMSO
                    mean            var
obs_cob    950914.083650  600151.061639
obs_green  301005.757935  301687.945684
obs_red    309699.705909  319892.963098


VHL
                   mean            var
obs_cob    1.038229e+06  687468.662850
obs_green  2.774448e+05  314741.866737
obs_red    3.601992e+05  362151.831322

change (VHL/DMSO)
obs_cob      1.091822
obs_green    0.921726
obs_red      1.163059
Name: mean, dtype: float64
sum_green_sum


DMSO
                   mean           var
obs_cob    2.205474e+06  1.779645e+06
obs_green  8.911486e+05  1.058596e+06
obs_red    1.438344e+05  2.120915e+05


VHL
                   mean           var
obs_cob    2.983196e+06  3.313045e+06
obs_green  1.030699e+06  1.670062e+06
obs_red    1.084936e+05  2.815154e+05

change (VHL/DMSO)
obs_cob      1.352632
obs_green    1.156596
obs_red      0.754295
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob    6.537189e+06  5.373758e+06
obs_green  5.323923e+05  7.164065e+05
obs_red    2.585041e+06  3.068551e+06


VHL
                   mean           var
obs_cob    9.987000e+06  8.728282e+06
obs_green  8.266140e+05  1.225971e+06
obs_red    3.817240e+06  4.561824e+06

change (VHL/DMSO)
obs_cob      1.527721
obs_green    1.552641
obs_red      1.476665
Name: mean, dtype: float64

______________________________
MEF2C-MYC_MEF2D-G1
mean_red_mean


DMSO
                  mean          var
obs_cob    9557.536400  4627.020817
obs_green  2854.575688  2200.355788
obs_red    8374.563788  3128.436098


VHL
                  mean          var
obs_cob    9313.612991  5827.048373
obs_green   433.010150   483.566179
obs_red    6650.370195  3152.833075

change (VHL/DMSO)
obs_cob      0.974478
obs_green    0.151690
obs_red      0.794115
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean          var
obs_cob    4211.982511  2669.456056
obs_green  7476.633417  3512.261436
obs_red    1232.972933   937.072490


VHL
                  mean          var
obs_cob    5246.102605   459.851330
obs_green  4659.242184  3412.406615
obs_red     509.468384   557.270189

change (VHL/DMSO)
obs_cob      1.245519
obs_green    0.623174
obs_red      0.413203
Name: mean, dtype: float64
area_sum


DMSO
                    mean           var
obs_cob    884400.857143  1.075235e+06
obs_green  158050.119052  1.484413e+05
obs_red    548561.772277  8.577962e+05


VHL
                   mean           var
obs_cob    2.579014e+06  2.692504e+06
obs_green  1.740104e+05  1.875073e+05
obs_red    1.312316e+06  9.781413e+05

change (VHL/DMSO)
obs_cob      2.916114
obs_green    1.100982
obs_red      2.392286
Name: mean, dtype: float64
sum_green_sum


DMSO
                   mean           var
obs_cob    1.606696e+06  5.770407e+05
obs_green  1.320934e+06  1.712366e+06
obs_red    8.672709e+05  2.568313e+06


VHL
                   mean           var
obs_cob    1.144254e+07  1.117395e+07
obs_green  1.102445e+06  2.204660e+06
obs_red    7.511028e+05  1.141429e+06

change (VHL/DMSO)
obs_cob      7.121783
obs_green    0.834595
obs_red      0.866053
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob    1.315621e+07  2.211324e+07
obs_green  4.978172e+05  7.559595e+05
obs_red    6.881957e+06  1.542599e+07


VHL
                   mean           var
obs_cob    3.281339e+07  4.129703e+07
obs_green  8.209288e+04  1.612319e+05
obs_red    1.143584e+07  1.158293e+07

change (VHL/DMSO)
obs_cob      2.494138
obs_green    0.164906
obs_red      1.661713
Name: mean, dtype: float64

______________________________
MEF2C-MYC_MEF2D-G2
mean_red_mean


DMSO
                  mean          var
obs_cob            NaN          NaN
obs_green   688.137433   734.254475
obs_red    7610.209331  1827.635770


VHL
                  mean          var
obs_cob            NaN          NaN
obs_green   328.590589   331.109043
obs_red    6778.450455  3269.002845

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.477507
obs_red      0.890705
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean         var
obs_cob            NaN         NaN
obs_green  2068.452373  403.338643
obs_red     146.525699  103.011130


VHL
                  mean         var
obs_cob            NaN         NaN
obs_green  2153.218727  558.837729
obs_red     120.195602   88.410221

change (VHL/DMSO)
obs_cob           NaN
obs_green    1.040981
obs_red      0.820304
Name: mean, dtype: float64
area_sum


DMSO
                   mean           var
obs_cob             NaN           NaN
obs_green  1.174522e+05  1.069600e+05
obs_red    1.225443e+06  1.299586e+06


VHL
                   mean           var
obs_cob             NaN           NaN
obs_green  1.084966e+05  1.126491e+05
obs_red    1.565342e+06  1.225300e+06

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.923751
obs_red      1.277368
Name: mean, dtype: float64
sum_green_sum


DMSO
                    mean            var
obs_cob              NaN            NaN
obs_green  261880.503597  271279.409053
obs_red    174010.166667  233631.542334


VHL
                    mean            var
obs_cob              NaN            NaN
obs_green  259597.039451  325547.568468
obs_red    219516.105263  288255.979569

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.991281
obs_red      1.261513
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob             NaN           NaN
obs_green  8.353937e+04  1.789144e+05
obs_red    1.127110e+07  1.410713e+07


VHL
                   mean           var
obs_cob             NaN           NaN
obs_green  3.608500e+04  5.699305e+04
obs_red    1.445703e+07  1.678381e+07

change (VHL/DMSO)
obs_cob           NaN
obs_green    0.431952
obs_red      1.282663
Name: mean, dtype: float64

______________________________
MED1-MYC_MEF2D-G1
mean_red_mean


DMSO
                  mean          var
obs_cob    5510.109937  3332.984615
obs_green  1073.179434   836.716868
obs_red    5139.465368  3570.673041


VHL
                  mean          var
obs_cob    1695.392138  1325.346047
obs_green   144.285806   268.002838
obs_red    2116.091886  1530.806937

change (VHL/DMSO)
obs_cob      0.307688
obs_green    0.134447
obs_red      0.411734
Name: mean, dtype: float64
mean_green_mean


DMSO
                  mean         var
obs_cob    1661.214482  780.378189
obs_green  2050.220394  916.378863
obs_red     160.923146  173.240429


VHL
                  mean         var
obs_cob    1191.141412  697.758682
obs_green  1366.922998  619.306561
obs_red     144.872026  229.709830

change (VHL/DMSO)
obs_cob      0.717030
obs_green    0.666720
obs_red      0.900256
Name: mean, dtype: float64
area_sum


DMSO
                    mean            var
obs_cob    885614.625000  633575.040870
obs_green  264318.678012  271400.892882
obs_red    571445.154639  754123.601032


VHL
                    mean            var
obs_cob    940672.538462  855595.551572
obs_green  232057.475251  211973.613314
obs_red    424022.663265  547264.810802

change (VHL/DMSO)
obs_cob      1.062169
obs_green    0.877946
obs_red      0.742018
Name: mean, dtype: float64
sum_green_sum


DMSO
                   mean            var
obs_cob    1.274181e+06  756477.545977
obs_green  6.542798e+05  908116.296412
obs_red    1.411824e+05  249909.236157


VHL
                    mean            var
obs_cob    957108.769231  893502.769827
obs_green  387841.311131  572947.434807
obs_red     75427.326531  132053.549000

change (VHL/DMSO)
obs_cob      0.751156
obs_green    0.592776
obs_red      0.534254
Name: mean, dtype: float64
sum_red_sum


DMSO
                   mean           var
obs_cob    6.042525e+06  8.102239e+06
obs_green  3.135642e+05  4.580838e+05
obs_red    4.951175e+06  7.319191e+06


VHL
                   mean           var
obs_cob    2.020074e+06  3.113093e+06
obs_green  3.531176e+04  9.965579e+04
obs_red    1.487815e+06  2.465998e+06

change (VHL/DMSO)
obs_cob      0.334310
obs_green    0.112614
obs_red      0.300497
Name: mean, dtype: float64

coloc specific

In [395]:
data = {}
for k in set(mdots.exp):
    print('______________________________\n')
    print(k)
    at = pd.DataFrame()
    
    for t in ['DMSO', 'VHL']:
        print(t)
        d = mdots[(mdots.exp==k)&(mdots.treat==t)]
        
        a = pd.DataFrame()
        
        # counts per coloc        
        e = [len(mgreen[mgreen.coloc_id.isin(d[(d["group"]==cell)&(d['class']=="cobinding")].index)]) for cell in set(d.group)]
        b = pd.DataFrame()
        b["color"] = ['green counts']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        # size per coloc        
        e = []
        [e.extend(mgreen[mgreen.coloc_id.isin(d[(d["group"]==cell)&(d['class']=="cobinding")].index)]['area_sum'].tolist()) for cell in set(d.group)]
        
        b = pd.DataFrame()
        b["color"] = ['green size']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        # size per coloc        
        e = []
        [e.extend(mred[mred.coloc_id.isin(d[(d["group"]==cell)&(d['class']=="cobinding")].index)]['area_sum'].tolist()) for cell in set(d.group)]
        
        b = pd.DataFrame()
        b["color"] = ['red size']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        a['type'] = "on coloc"
        a['cond'] = t
        at = at.append(a)
    data[k] = at.reset_index(drop=True)
    
# adding chi2
for k,val in data.items():
    for i in set(val['type']):
        e = " |"
        for u in ['green counts', 'green size', 'red size']:#set(val['color']):
            a = val[(val['type'] == i) & (val['color'] == u) & (val['cond'] == "DMSO")]["signal"].tolist()
            b = val[(val['type'] == i) & (val['color'] == u) & (val['cond'] == "VHL")]["signal"].tolist()
            r = ttest_ind(a, b, equal_var=False)
            e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
        val.loc[val[val['type'] == i].index, 'type'] = i + e +"|"
    data[k] = val
______________________________

MEF2D-MYC_MEF2D-G1
DMSO
VHL
______________________________

MEF2D-MEF2C-G2
DMSO
VHL
______________________________

MEF2D-MEF2C-G1
DMSO
VHL
______________________________

MEF2C-MYC_MEF2D-G1
DMSO
VHL
______________________________

MEF2C-MYC_MEF2D-G2
DMSO
VHL
______________________________

MED1-MYC_MEF2D-G1
DMSO
VHL
<ipython-input-395-8f25db698edf>:51: RuntimeWarning: divide by zero encountered in double_scalars
  e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
/opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3419: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/opt/conda/lib/python3.8/site-packages/numpy/core/_methods.py:188: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
<ipython-input-395-8f25db698edf>:51: RuntimeWarning: invalid value encountered in double_scalars
  e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
In [396]:
for k, val in data.items():
    g = sns.catplot(kind="violin", x="type", y="signal", hue="cond", col="color", data=val, palette="muted", sharey=False, height=7)
    g.fig.subplots_adjust(top=0.86)
    g.fig.suptitle(k)
    plt.show()
    g.savefig(folder+k+"_violin_oncobinding.pdf")

on all

In [397]:
# compute difference in number/surface/max/min/mean(val) of green /red across cells and cobindings
data = {}
typ = [("sum_green_sum", "green sum"), ('sum_red_sum', "red sum"), ('mean_red_mean', "red mean"), ('mean_green_mean', "green mean"), ("area_sum", "size")]
for k in set(mdots.exp):
    print(k)
    print('______________________________\n')
    at = pd.DataFrame()
    
    for t in ['DMSO', 'VHL']:
        print(t)
        d = mdots[(mdots.exp==k)&(mdots.treat==t)]
        
        # counts per cell
        a = pd.DataFrame()
        b = pd.DataFrame()
        print("\ncount greens per cell:")
        e = [len(d[(d["group"]==cell)&(d['class']=="green")]) for cell in set(d.group)]
        print(min(e))
        print(np.mean(e), np.sqrt(np.var(e)))
        
        b["color"] = ['on green']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        b = pd.DataFrame()
        e = [len(d[(d["group"]==cell)&(d['class']=="red")]) for cell in set(d.group)]
        b["color"] = ['on red']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        b = pd.DataFrame()
        e = [len(d[(d["group"]==cell)&(d['class']=="cobinding")]) for cell in set(d.group)]
        b["color"] = ['on coloc']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        a['type'] = "counts"
        a['cond'] = t
        at = at.append(a)
        
        # TOTAL signal
        
        b = pd.DataFrame()
        print("\ntotal signal greens per cell:")
        e = [sum(d[(d["group"]==cell)&(d['class']=="green")]['sum_green_sum']) for cell in set(d.group)]
        print(min(e))
        print(np.mean(e), np.sqrt(np.var(e)))
        
        b["color"] = ['on green']*len(e)
        b['signal'] = e
        a.append(b)
        
        b = pd.DataFrame()
        e = [sum(d[(d["group"]==cell)&(d['class']=="red")]['sum_red_sum']) for cell in set(d.group)]
        b["color"] = ['on red']*len(e)
        b['signal'] = e
        a = a.append(b)
        
        b = pd.DataFrame()
        e = [sum(d[(d["group"]==cell)&(d['class']=="cobinding")]['sum_green_sum']) for cell in set(d.group)]
        b["color"] = ['on coloc']*len(e)
        b['signal'] = e
        a.append(b)
        
        a['type'] = "total sum"
        a['cond'] = t
        at = at.append(a)
        
        for (col,name) in typ:
            print(name)
            a = pd.DataFrame()
            print('\nsignal in green:')
            e = d[d['class']=="green"][col].tolist()
            print(np.mean(e), np.sqrt(np.var(e)))
            b = pd.DataFrame()
            b['signal'] = e
            b['color'] = "on green"
            a = a.append(b)
            
            print('\nsignal in red:')
            e = d[d['class']=="red"][col].tolist()
            print(np.mean(e), np.sqrt(np.var(e)))
            
            b = pd.DataFrame()
            b['signal'] =  e
            b['color'] = "on red"
            a = a.append(b)

            print('\nsignal in cobinding:')
            e = d[d['class']=="cobinding"][col].tolist()
            print(np.mean(e), np.sqrt(np.var(e)))
            
            b = pd.DataFrame()
            b['signal'] = e
            b['color'] = "on coloc"           
            print('\n\n')
            a = a.append(b)
            a['type']=name
            a['cond'] = t
            at = at.append(a)
        print('____________\n')
    data[k] = at.reset_index(drop=True)
    
# adding chi2
for k,val in data.items():
    for i in set(val['type']):
        e = " |"
        for u in ['on green', "on red", "on coloc"]:#set(val['color']):
            a = val[(val['type'] == i) & (val['color'] == u) & (val['cond'] == "DMSO")]["signal"].tolist()
            b = val[(val['type'] == i) & (val['color'] == u) & (val['cond'] == "VHL")]["signal"].tolist()
            r = ttest_ind(a, b, equal_var=False)
            e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
        val.loc[val[val['type'] == i].index, 'type'] = i + e +"|"
    data[k] = val
MEF2D-MYC_MEF2D-G1
______________________________

DMSO

count greens per cell:
0
141.46153846153845 200.01504381290746

total signal greens per cell:
0
99031231.6923077 236900013.53370842
green sum

signal in green:
700057.6465470365 1121721.1667776324

signal in red:
260275.75438596492 332112.3998030217

signal in cobinding:
2021220.5 525499.5



red sum

signal in green:
159349.87737901034 189261.0585052119

signal in red:
5818733.140350877 6536614.274329838

signal in cobinding:
16920444.5 5066850.5



red mean

signal in green:
779.517516927957 627.628343530604

signal in red:
4228.277974162644 1318.289370855427

signal in cobinding:
6545.9709892574065 1249.4890466124734



green mean

signal in green:
2426.1651210172417 868.6293373264833

signal in red:
250.03143580078984 231.16353095121735

signal in cobinding:
1009.4053917086851 82.80455460059301



size

signal in green:
228912.03126699294 246789.3091705746

signal in red:
1117670.894736842 959748.0683276501

signal in cobinding:
2634871.0 407626.0



____________

VHL

count greens per cell:
0
5.08 7.036590083271869

total signal greens per cell:
0
2695079.64 5966599.797441131
green sum

signal in green:
530527.4881889764 1222449.32914396

signal in red:
51540.56603773585 73077.20327638756

signal in cobinding:
nan nan



red sum

signal in green:
42495.22834645669 68482.18807458834

signal in red:
4430263.0 5992892.894728262

signal in cobinding:
nan nan



red mean

signal in green:
208.25300224987149 155.50859699698134

signal in red:
2756.07191820443 1305.9405295590375

signal in cobinding:
nan nan



green mean

signal in green:
1818.7946396739349 704.4060912158607

signal in red:
48.52473177679123 83.70704076892657

signal in cobinding:
nan nan



size

signal in green:
198950.968503937 250139.719313214

signal in red:
1166581.7735849055 939189.3617631942

signal in cobinding:
nan nan



____________

MEF2D-MEF2C-G2
______________________________

DMSO

count greens per cell:
53
128.34615384615384 55.39782160927679
/opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3419: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/opt/conda/lib/python3.8/site-packages/numpy/core/_methods.py:188: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
/opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3702: RuntimeWarning: Degrees of freedom <= 0 for slice
  return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
/opt/conda/lib/python3.8/site-packages/numpy/core/_methods.py:221: RuntimeWarning: invalid value encountered in true_divide
  arrmean = um.true_divide(arrmean, div, out=arrmean, casting='unsafe',
/opt/conda/lib/python3.8/site-packages/numpy/core/_methods.py:253: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)
total signal greens per cell:
27364638.0
78501223.34615384 41812011.31675683
green sum

signal in green:
611636.7416841474 860230.7170667215

signal in red:
147096.97910135842 308021.5847259859

signal in cobinding:
1891262.9208211144 2020996.1362675603



red sum

signal in green:
743504.9044051543 1102506.412891556

signal in red:
3549732.606583072 5341065.606951432

signal in cobinding:
10671614.680351906 12027278.850823194



red mean

signal in green:
3731.302142481981 2414.627101602329

signal in red:
9599.184439853983 2838.2950005250505

signal in cobinding:
10135.384977420616 3213.899255092863



green mean

signal in green:
2895.2830202439886 1173.0706978731284

signal in red:
432.18738045157244 494.10793539484786

signal in cobinding:
2197.189243360367 1191.3484889493018



size

signal in green:
195295.1890919988 229656.21683121135

signal in red:
331091.5773249739 380985.6102856037

signal in cobinding:
921057.6656891495 751433.6247676181



____________

VHL

count greens per cell:
25
68.28571428571429 24.740833525518394

total signal greens per cell:
7251268.0
41835940.333333336 36772346.310580865
green sum

signal in green:
612660.2140864714 1245647.6201689532

signal in red:
558755.4646209386 795549.3785030752

signal in cobinding:
2631495.0754716983 4004049.466735811



red sum

signal in green:
313269.0181311018 579923.7049128368

signal in red:
3039866.559566787 3555743.306822428

signal in cobinding:
5887194.245283019 4997829.411580828



red mean

signal in green:
2683.9333701737974 1855.1452797222355

signal in red:
9392.507190298598 2682.4694258278337

signal in cobinding:
8855.578484528723 2264.9128605528217



green mean

signal in green:
4603.154941808157 2912.708549306905

signal in red:
1857.811556325861 1429.2355182837625

signal in cobinding:
3852.9197674576 2395.7052663307354



size

signal in green:
107253.67573221757 118364.00136968515

signal in red:
295266.70505415165 287732.8894258694

signal in cobinding:
615574.7452830189 417998.98231484543



____________

MEF2D-MEF2C-G1
______________________________

DMSO

count greens per cell:
51
207.41666666666666 108.98352959150398

total signal greens per cell:
19824671.0
184839077.25 136376922.8519569
green sum

signal in green:
891148.6247488952 1058489.6959678358

signal in red:
143834.3743797925 212043.64879275596

signal in cobinding:
2205474.4600760457 1776258.5980336738



red sum

signal in green:
532392.2677782242 716334.5024935205

signal in red:
2585040.5561569687 3067859.0275081596

signal in cobinding:
6537188.551330798 5363532.061398725



red mean

signal in green:
1726.344314442008 1243.3488091848437

signal in red:
7564.666382844557 1459.8753992281263

signal in cobinding:
6430.033733374528 1841.1380846765305



green mean

signal in green:
2621.144502070123 778.1316816503596

signal in red:
468.0889096184756 437.7030554768811

signal in cobinding:
2225.7742804503855 864.6419332013073



size

signal in green:
301005.7579349136 301657.64203806425

signal in red:
309699.70590888587 319820.809501891

signal in cobinding:
950914.0836501902 599009.0033984939



____________

VHL

count greens per cell:
16
136.52173913043478 95.37881888882669

total signal greens per cell:
6773822.0
140712797.0 155045000.68222615
green sum

signal in green:
1030698.8315286625 1669795.7359231021

signal in red:
108493.59991699523 281486.20823457814

signal in cobinding:
2983195.925072046 3308267.922746277



red sum

signal in green:
826613.9910828025 1225775.5022777708

signal in red:
3817239.5891263746 4561351.147082311

signal in cobinding:
9987000.403458213 8715695.954729458



red mean

signal in green:
2884.5829950319194 2150.775296970291

signal in red:
9605.706020508804 2479.6364324761425

signal in cobinding:
9092.451256095837 2940.4132293569032



green mean

signal in green:
3173.349356940772 1752.8977042147044

signal in red:
283.99360348236297 478.72337965716713

signal in cobinding:
2708.350562333958 1879.4801373633732



size

signal in green:
277444.8455414013 314691.7446146521

signal in red:
360199.15812409215 362114.25395964156

signal in cobinding:
1038228.9250720461 686477.3592877315



____________

MEF2C-MYC_MEF2D-G1
______________________________

DMSO

count greens per cell:
9
92.0909090909091 60.73138255009745

total signal greens per cell:
6717799.0
121645980.38181818 132960380.7308926
green sum

signal in green:
1320933.6467917077 1712196.7297722609

signal in red:
867270.8514851485 2555567.08906511

signal in cobinding:
1606695.5714285714 534235.9238359176



red sum

signal in green:
497817.2306021718 755884.8356359754

signal in red:
6881956.801980198 15349438.129323721

signal in cobinding:
13156207.857142856 20472883.5467297



red mean

signal in green:
2854.5756881914176 2200.1385654956366

signal in red:
8374.563787848372 3112.9102647151153

signal in cobinding:
9557.536399742452 4283.788874805996



green mean

signal in green:
7476.633416750497 3511.914700054709

signal in red:
1232.9729327710909 932.4219779646297

signal in cobinding:
4211.982511135259 2471.436072395692



size

signal in green:
158050.11905231985 148426.59828703498

signal in red:
548561.7722772277 853539.096336662

signal in cobinding:
884400.8571428572 995473.9550293804



____________

VHL

count greens per cell:
5
93.6923076923077 60.57339428061264

total signal greens per cell:
1808637.0
103290584.07692307 187594181.90483424
green sum

signal in green:
1102444.657635468 2204434.150416132

signal in red:
751102.7894736842 1133895.1596464464

signal in cobinding:
11442537.5 7901176.5



red sum

signal in green:
82092.88136288998 161215.36195874005

signal in red:
11435840.157894736 11506472.284079606

signal in cobinding:
32813394.0 29201410.0



red mean

signal in green:
433.0101499651145 483.51654911829684

signal in red:
6650.370194782501 3132.022068423323

signal in cobinding:
9313.612991047448 4120.345418896565



green mean

signal in green:
4659.242184275976 3412.0563915198054

signal in red:
509.46838386972206 553.5917978495828

signal in cobinding:
5246.102604553308 325.16399382644704



size

signal in green:
174010.36699507388 187488.0859697855

signal in red:
1312316.5 971684.8628721159

signal in cobinding:
2579014.0 1903888.0



____________

MEF2C-MYC_MEF2D-G2
______________________________

DMSO

count greens per cell:
0
13.238095238095237 18.824767783060906

total signal greens per cell:
0
3466799.0476190476 5633002.665123735
green sum

signal in green:
261880.5035971223 270791.0568850306

signal in red:
174010.16666666666 230833.45885404013

signal in cobinding:
nan nan



red sum

signal in green:
83539.36690647482 178592.34217253307

signal in red:
11271101.238095239 13938173.228532149

signal in cobinding:
nan nan



red mean

signal in green:
688.1374333124901 732.9326835777066

signal in red:
7610.209331414514 1805.747127353867

signal in cobinding:
nan nan



green mean

signal in green:
2068.4523728658996 402.61256003248354

signal in red:
146.52569918705416 101.77741961259207

signal in cobinding:
nan nan



size

signal in green:
117452.1726618705 106767.46627840222

signal in red:
1225443.0476190476 1284021.449816477

signal in cobinding:
nan nan



____________

VHL

count greens per cell:
0
23.32 29.94490941712798

total signal greens per cell:
0
6053802.96 8691373.295328477
green sum

signal in green:
259597.03945111492 325268.24832307734

signal in red:
219516.1052631579 284437.8509961442

signal in cobinding:
nan nan



red sum

signal in green:
36084.99828473413 56944.149203340276

signal in red:
14457026.789473685 16561493.883778341

signal in cobinding:
nan nan



red mean

signal in green:
328.5905887577564 330.8249515887265

signal in red:
6778.450454602423 3225.702882558013

signal in cobinding:
nan nan



green mean

signal in green:
2153.2187274158514 558.3582460517542

signal in red:
120.19560150964001 87.23917280753538

signal in cobinding:
nan nan



size

signal in green:
108496.5986277873 112552.45222638048

signal in red:
1565341.894736842 1209070.5014758192

signal in cobinding:
nan nan



____________

MED1-MYC_MEF2D-G1
______________________________

DMSO

count greens per cell:
87
263.2244897959184 108.87500140464817

total signal greens per cell:
50052452.0
172222474.63265306 93201500.23055278
green sum

signal in green:
654279.8307489533 908081.0919656417

signal in red:
141182.43298969071 248617.7069112587

signal in cobinding:
1274180.8125 732456.2343371803



red sum

signal in green:
313564.2211970848 458066.08628637175

signal in red:
4951174.958762887 7281365.639314521

signal in cobinding:
6042524.5625 7844959.413438223



red mean

signal in green:
1073.1794341061795 836.684431771915

signal in red:
5139.4653675697455 3552.219827298448

signal in cobinding:
5510.109936557839 3227.148476295088



green mean

signal in green:
2050.220393758569 916.3433382046345

signal in red:
160.92314600665676 172.3451236459121

signal in cobinding:
1661.2144818514444 755.5979323974893



size

signal in green:
264318.6780120949 271390.3716326034

signal in red:
571445.1546391753 750226.2953506864

signal in cobinding:
885614.625 613456.3954657734



____________

VHL

count greens per cell:
64
224.6078431372549 157.03915815294974

total signal greens per cell:
12249984.0
87112200.37254901 77368409.23090303
green sum

signal in green:
387841.3111305107 572922.4256433384

signal in red:
75427.32653061225 131378.07884691274

signal in cobinding:
957108.7692307692 858449.6937126883



red sum

signal in green:
35311.76045395024 99651.4363537487

signal in red:
1487814.8673469387 2453384.319334307

signal in cobinding:
2020074.3076923077 2990963.357067737



red mean

signal in green:
144.28580559897807 267.9911393595846

signal in red:
2116.0918862793064 1522.9766707728022

signal in cobinding:
1695.3921380884703 1273.3512938129684



green mean

signal in green:
1366.9229984371534 619.2795285219264

signal in red:
144.8720257300083 228.53483547508912

signal in cobinding:
1191.1414118331077 670.3848577395777



size

signal in green:
232057.4752509821 211964.36066298254

signal in red:
424022.6632653061 544465.4839497276

signal in cobinding:
940672.5384615385 822029.6164624031



____________

<ipython-input-397-a0399e1665f2>:112: RuntimeWarning: divide by zero encountered in double_scalars
  e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
<ipython-input-397-a0399e1665f2>:112: RuntimeWarning: invalid value encountered in double_scalars
  e+= u + ": ("f"{np.mean(a)/np.mean(b):.1f}, "f"{r[1]:.1e}), "
In [402]:
v
Out[402]:
color signal type cond
0 on green 12.0 counts |on green: (0.6, 1.8e-01), on red: (1.3... DMSO
1 on green 11.0 counts |on green: (0.6, 1.8e-01), on red: (1.3... DMSO
2 on green 6.0 counts |on green: (0.6, 1.8e-01), on red: (1.3... DMSO
3 on green 23.0 counts |on green: (0.6, 1.8e-01), on red: (1.3... DMSO
4 on green 2.0 counts |on green: (0.6, 1.8e-01), on red: (1.3... DMSO
... ... ... ... ...
5022 on red 3426607.0 size |on green: (1.1, 2.6e-01), on red: (0.8, ... VHL
5023 on red 147959.0 size |on green: (1.1, 2.6e-01), on red: (0.8, ... VHL
5024 on red 1829416.0 size |on green: (1.1, 2.6e-01), on red: (0.8, ... VHL
5025 on red 2661327.0 size |on green: (1.1, 2.6e-01), on red: (0.8, ... VHL
5026 on red 2128277.0 size |on green: (1.1, 2.6e-01), on red: (0.8, ... VHL

5027 rows × 4 columns

In [403]:
l
Out[403]:
'green sum |on green: (1.0, 9.1e-01), on red: (0.8, 4.4e-01), on coloc: (nan, nan), |'
In [ ]:
 
In [409]:
for k, v in data.items():
    for l in set(v['type']):
        for m in set(v['color']):
            try:
                ax = sns.violinplot(data = v[(v['type']==l)&(v['color']==m)], x="color", y="signal", hue="cond", palette="muted")
            except ValueError:
                print('no data to plot that one')
                continue
            plt.title(k+" "+l.split('|')[0]+""+l.split(m)[-1].split('),')[0]+") ")
            
            plt.show()
            
            ax.get_figure().savefig(folder+k+'_single_'+l.split(' |')[0]+"_"+m+".pdf")
In [410]:
for k, val in data.items():
    g = sns.catplot(kind="violin", x="color", y="signal", hue="cond", col="type", data=val, palette="muted", sharey=False, height=7)
    g.fig.subplots_adjust(top=0.86)
    g.fig.suptitle(k)
    plt.yscale('log')
    plt.show()
    g.savefig(folder+k+"_violin_all.pdf")
In [411]:
for val in ['MEF2D-MEF2C-G2','MEF2D-MEF2C-G1']:
    a = data[val]
    for v in ['DMSO','VHL']:
        f = int(a[(a.color=="on green")&(a.cond==v)&(a.type.str.contains('counts'))].signal.mean())
        e = int(a[(a.color=="on red")&(a.cond==v)&(a.type.str.contains('counts'))].signal.mean())
        i = int(a[(a.color=="on coloc")&(a.cond==v)&(a.type.str.contains('counts'))].signal.mean())
        c = f
        f = [u for u in range(f)]
        pe = [u for u in range(c, e+c)]
        c+=e
        e=pe
        e.extend([u for u in range(c, c+i)])
        f.extend([u for u in range(c, c+i)])
        plot.venn([set(e),set(f)], ["red", "green"], title=v)
        plt.savefig(folder+'MEF2D-MEF2C_venn'+v+'_mean_percell.pdf')
/home/jeremie/.local/lib/python3.8/site-packages/venn/_backwards_compatibility.py:15: UserWarning: `get_labels()` is retained for backwards compatibility; use `generate_petal_labels()` or the higher level `venn()` instead
  warn((
/home/jeremie/.local/lib/python3.8/site-packages/venn/_backwards_compatibility.py:30: UserWarning: `venn2()` is retained for backwards compatibility; use `venn()` instead
  warn((
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>

likelyhood of coloc compared to expectation for each experiment & between conditions

In [412]:
# compute dot likelyhood of coloc compared to expectation for each experiment &  between conditions
enrichment = {}

for k in set(mdots.exp):
    count = []
    fisher = []
    print('______________________________')
    print(k)
    for e in ['DMSO', 'VHL']:
        d = mdots[(mdots.exp==k)&(mdots.treat==e)]
        allredcount = len(d[d['class']!="green"])
        try:
            obs_cob = len(d[d['class']=="cobinding"])/allredcount
        except ZeroDivisionError:
            print('no red dot')
            continue
        obs_green = d[d['class']=="green"].area_sum.sum() * call_scale
        pred_cob = obs_green / mcells[(mcells.exp==k)&(mcells.treat==e)].area_sum.sum()
        #print(obs_cob, obs_green, pred_cob)
        count.append(obs_cob/pred_cob)
        print(e+": "+str(obs_cob/pred_cob))
        a = pred_cob*allredcount
        b = obs_cob*allredcount
        f = fisher_exact(np.array([[b, allredcount - b], [a, allredcount - a]], dtype=int))
        if f[0] is np.inf:
            f = (obs_cob/pred_cob, f[1])
        fisher.append(f)
    enrichment[k] = fisher
    print("change: "+str(count[1]/count[0]))
______________________________
MEF2D-MYC_MEF2D-G1
DMSO: 7.5614665173005715
VHL: 0.0
change: 0.0
______________________________
MEF2D-MEF2C-G2
DMSO: 11.757757882531092
VHL: 30.82590410972557
change: 2.621750202521578
______________________________
MEF2D-MEF2C-G1
DMSO: 9.490256189760313
VHL: 8.729944847809131
change: 0.9198850561303568
______________________________
MEF2C-MYC_MEF2D-G1
DMSO: 8.603712878385402
VHL: 3.22860559916173
change: 0.3752572458888958
______________________________
MEF2C-MYC_MEF2D-G2
DMSO: 0.0
VHL: 0.0
change: nan
______________________________
MED1-MYC_MEF2D-G1
DMSO: 7.776734633454093
VHL: 9.2874439470974
change: 1.1942601084965032
<ipython-input-412-ed259571b9b8>:29: RuntimeWarning: invalid value encountered in double_scalars
  print("change: "+str(count[1]/count[0]))
<Figure size 432x288 with 0 Axes>
In [413]:
x=[]
y=[]
e=[]
for k, val in enrichment.items():
    x.append(k+'\nDMSO')
    y.append(val[0][0])
    e.append(val[0][1])
    x.append(k+'\nVHL')
    y.append(val[1][0])
    e.append(val[1][1])
plt.errorbar(x, y, e, linestyle='None', marker='.')
plt.xticks(rotation = 40) # Rotates X-Axis Ticks by 45-degrees
plt.savefig(folder+'enrichments.pdf')

plot of averaged binned signal by distance from focis

In [606]:
# make a plot of averaged binned signal strength by distance from locis
twodists, dists = fish.computeDistsFromClass(agreen, mred, conds=['DMSO', 'VHL'], groupcol="group", sclass='green', signal="mean_green", area="area")
MEF2D-MYC_MEF2D-G1 DMSO
MEF2D-MYC_MEF2D-G1 VHL
MEF2D-MEF2C-G2 DMSO
MEF2D-MEF2C-G2 VHL
MEF2D-MEF2C-G1 DMSO
MEF2D-MEF2C-G1 VHL
MEF2C-MYC_MEF2D-G1 DMSO
MEF2C-MYC_MEF2D-G1 VHL
MEF2C-MYC_MEF2D-G2 DMSO
MEF2C-MYC_MEF2D-G2 VHL
MED1-MYC_MEF2D-G1 DMSO
MED1-MYC_MEF2D-G1 VHL
100%
In [415]:
size=1500
bins = 150
defa = size/bins
for k, v in dists.items():
    plt.title(k)
    scale = ((4.19*(defa+v[0][v[0]<size]))**3)-((4.19*(v[0][v[0]<size]))**3)
    ax = sns.histplot(x=v[0][v[0]<size], weights=v[1][v[0]<size]/scale, bins=bins, fill=True)
    plt.axvline(x=180, color="red")
    ax.set(xlim=(-20,None))
    plt.show()
    ax.get_figure().savefig(folder+k+'_1D_distances_from_red.pdf')
In [587]:
norm = {'MED1-MYC_MEF2D-G1':(2000,5000), 
        'MED1-MYC_MEF2D-G2':(2000,5000),
        'MEF2C-MYC_MEF2D-G2':(2000,5000),
        'MEF2C-MYC_MEF2D-G1':(2000,5000),
        'MEF2D-MYC_MEF2D-G1':(2000,5000)
       }
In [675]:
for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2']:#,'MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
    fish.drawDots({i: twodists[i] for i in twodists.keys() if k in i}, scenter=25, size=1400, zsize=800, folder=folder+'greenall_', signal="mean_green",area="area", sizes=(100,250), alpha=0.1, levels=20, norm_dots=norm[k], norm=norm[k], vmin=0.5e-7, vmax=1.2e-7, second=lambda x: x.index.isin(adots[adots.index.isin(x.index.tolist()) & adots.m_id.isin(mdots[mdots['class'] == "cobinding"].index)].index))
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1676: UserWarning: `shade_lowest` is now deprecated in favor of `thresh`. Setting `thresh=0.05`, but please update your code.
  warnings.warn(msg, UserWarning)
adding second color
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1676: UserWarning: `shade_lowest` is now deprecated in favor of `thresh`. Setting `thresh=0.05`, but please update your code.
  warnings.warn(msg, UserWarning)
adding second color
In [467]:
norm = {'MED1-MYC_MEF2D-G1':(1000,5000), 
        'MED1-MYC_MEF2D-G2':(2000,5000),
        'MEF2C-MYC_MEF2D-G2':(2000,5000),
        'MEF2C-MYC_MEF2D-G1':(2000,5000),
        'MEF2D-MYC_MEF2D-G1':(2000,5000)
       }
<Figure size 432x288 with 0 Axes>
In [483]:
for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
    fish.drawDots({i: twodists[i] for i in twodists.keys() if k in i}, scenter=85, size=400, zsize=600, folder=folder+'coloconly_', signal="mean_green", area="area", sizes=(400,850), palette=sns.light_palette("orange", as_cmap=True), alpha=0.4, norm=norm[k])
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3079             try:
-> 3080                 return self._engine.get_loc(casted_key)
   3081             except KeyError as err:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'mean_green'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-483-c598cfea6639> in <module>
      1 for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
----> 2     fish.drawDots({i: twodists[i] for i in twodists.keys() if k in i}, scenter=85, size=400, zsize=600, folder=folder+'coloconly_', signal="mean_green", area="area", sizes=(400,850), palette=sns.light_palette("orange", as_cmap=True), alpha=0.4, norm=norm[k])

~/genepy/genepy/imaging/fish.py in drawDots(dists, scenter, size, zsize, folder, signal, area, norm, **kwargs)
     46         m = []
     47         for i, (k,a) in enumerate(dists.items()):
---> 48                 m.append(a[signal].max())
     49         for i, (k,a) in enumerate(dists.items()):
     50                 a = a.copy()

/opt/conda/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3022             if self.columns.nlevels > 1:
   3023                 return self._getitem_multilevel(key)
-> 3024             indexer = self.columns.get_loc(key)
   3025             if is_integer(indexer):
   3026                 indexer = [indexer]

/opt/conda/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3080                 return self._engine.get_loc(casted_key)
   3081             except KeyError as err:
-> 3082                 raise KeyError(key) from err
   3083 
   3084         if tolerance is not None:

KeyError: 'mean_green'
In [474]:
# make a plot of averaged binned signal strength by distance from locis
twodists, _ = fish.computeDistsFromClass(ared[~ared.group.str.contains("MEF2D-MEF2C")], mred[~mred.group.str.contains("MEF2D-MEF2C")], conds=['DMSO', 'VHL'], groupcol="group", sclass='red', signal="mean_red", area="area")
MEF2D-MYC_MEF2D-G1 DMSO
MEF2D-MYC_MEF2D-G1 VHL
MEF2C-MYC_MEF2D-G2 DMSO
MEF2C-MYC_MEF2D-G2 VHL
MED1-MYC_MEF2D-G1 DMSO
MED1-MYC_MEF2D-G1 VHL
MEF2C-MYC_MEF2D-G1 DMSO
MEF2C-MYC_MEF2D-G1 VHL
99%
In [479]:
norm = {'MED1-MYC_MEF2D-G1':(2000,15000), 
        'MED1-MYC_MEF2D-G2':(4000,20000),
        'MEF2C-MYC_MEF2D-G2':(4000,20000),
        'MEF2C-MYC_MEF2D-G1':(4000,20000),
        'MEF2D-MYC_MEF2D-G1':(2000,8000)
       }
<Figure size 432x288 with 0 Axes>
In [637]:
for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
    fish.drawDots({i: twodists[i] for i in twodists.keys() if k in i}, scenter=75, size=400, zsize=600, folder=folder+'redonly', signal="mean_red", area="area", sizes=(400,850), palette=sns.light_palette("red", as_cmap=True), alpha=0.05, norm=norm[k])
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-637-cb2277e32c47> in <module>
      1 for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
----> 2     fish.drawDots({i: twodists[i] for i in twodists.keys() if k in i}, scenter=75, size=400, zsize=600, folder=folder+'redonly', signal="mean_red", area="area", sizes=(400,850), palette=sns.light_palette("red", as_cmap=True), alpha=0.05, norm=norm[k])

~/genepy/genepy/imaging/fish.py in drawDots(dists, scenter, size, zsize, folder, signal, levels, area, norm, second, color, seccolor, **kwargs)
     63                 #ax = sns.scatterplot(data=a, x='x', y='y', hue_norm=(None,max(m)) if norm is None else norm,
     64                                                                                         #       hue=signal, size=area, palette=color, **kwargs)
---> 65 		ax=sns.kdeplot(data=a[['x', 'y', signal]].astype(float),
     66                  x='x', fill=True, y='y', cbar=False,weights=signal, color="seagreen",
     67                  shade_lowest=False, levels=levels, hue_norm=(None, max(m)/sca))

/opt/conda/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3028             if is_iterator(key):
   3029                 key = list(key)
-> 3030             indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
   3031 
   3032         # take() does not accept boolean indexers

/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
   1264             keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
   1265 
-> 1266         self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
   1267         return keyarr, indexer
   1268 

/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
   1314             if raise_missing:
   1315                 not_found = list(set(key) - set(ax))
-> 1316                 raise KeyError(f"{not_found} not in index")
   1317 
   1318             not_found = key[missing_mask]

KeyError: "['mean_red'] not in index"
In [ ]:
size=400
lim = 100
bins=60
s = 25
maxv=10
for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
    for u, v in {i: twodists[i] for i in twodists.keys() if k in i}.items():
        p = sns.jointplot(data=v[(abs(v.x)<size) & (abs(v.y)<size)], x="x", y="y",  kind="hex", color="red", joint_kws={'weights':"mean_red"}, xlim=(-lim,lim), ylim=(-lim,lim), gridsize=bins, vmax=maxv)
        p.plot_marginals(sns.distplot, rug=True, hist_kws={"range":(0,maxv)})
        p.plot_joint(sns.kdeplot, color="black", xlim=(-lim,lim), ylim=(-lim,lim), weights="mean_red", linewidth=1, alpha=.5)

        # gridsize=50, kind="hex", marginal_kws=dict(bins=50))
        # hue="sum_green_sum")
        x_values = [0]#, 2, 3, 4]
        y_values = [0]#, 0, 0, 0]
        #p.ax_joint.plot(x_values, y_values, 'o', ms=s, markerfacecolor="None",
        #    markeredgecolor='white', markeredgewidth=1)
        p.fig.suptitle(u)
        plt.show()
    
        p.savefig(folder+u+'_2D_distances_from_red.pdf')
    break
In [584]:
size=200
lim = 100
bins=60
s = 25
for k in ['MED1-MYC_MEF2D-G1','MED1-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G2','MEF2C-MYC_MEF2D-G1','MEF2D-MYC_MEF2D-G1']:
    for u, v in {i: twodists[i] for i in twodists.keys() if k in i}.items():
        p = sns.jointplot(data=v[(abs(v.x)<size) & (abs(v.y)<size)], x="x", y="y",  kind="hex", color="red",  xlim=(-lim,lim), ylim=(-lim,lim), gridsize=bins, vmin=0, vmax=20)
        sns.kdeplot(data=v[(abs(v.x)<size) & (abs(v.y)<size)][["x",'y','mean_red']].astype(float), x="x", y="y", color="black", xlim=(-lim,lim), ylim=(-lim,lim), weights="mean_red", linewidth=1, alpha=.5)

        # gridsize=50, kind="hex", marginal_kws=dict(bins=50))
        # hue="sum_green_sum")
        x_values = [0]#, 2, 3, 4]
        y_values = [0]#, 0, 0, 0]
        #p.ax_joint.plot(x_values, y_values, 'o', ms=s, markerfacecolor="None",
        #    markeredgecolor='white', markeredgewidth=1)
        p.fig.suptitle(u)
        plt.show()
    
        p.savefig(folder+u+'_2D_distances_from_red.pdf')
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
/opt/conda/lib/python3.8/site-packages/seaborn/distributions.py:1182: UserWarning: The following kwargs were not used by contour: 'xlim', 'ylim', 'linewidth'
  cset = contour_func(
In [585]:
# apply plot to old data from juliana    
In [ ]:
# make a nice fake plot
sns.plot()
In [194]:
mv $folder '../results/FishSuperResColoc/plots_v3_withfilter/'

saving

In [200]:
! cd .. && git add . && git commit -m "adding whiskers" && git push
[master a1525e5] adding whiskers
 292 files changed, 324615 insertions(+), 10805 deletions(-)
 rewrite notebooks/Fish_SuperRes.ipynb (66%)
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